Overview

Dataset statistics

Number of variables19
Number of observations195
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.1 KiB
Average record size in memory152.7 B

Variable types

Text1
Numeric17
Categorical1

Alerts

Co2-Emissions per ton is highly overall correlated with GDP and 1 other fieldsHigh correlation
GDP is highly overall correlated with Co2-Emissions per ton and 1 other fieldsHigh correlation
Population is highly overall correlated with Co2-Emissions per ton and 1 other fieldsHigh correlation
Infant mortality is highly overall correlated with Minimum wage and 6 other fieldsHigh correlation
Minimum wage is highly overall correlated with Infant mortality and 5 other fieldsHigh correlation
temperature is highly overall correlated with Infant mortality and 6 other fieldsHigh correlation
Gini's index is highly overall correlated with Prevalence of moderate or severe food insecurity in the total population (percent) (2022)High correlation
GDP per capita is highly overall correlated with Infant mortality and 6 other fieldsHigh correlation
Human Development Index (2021) is highly overall correlated with Infant mortality and 7 other fieldsHigh correlation
ln GDP per capita is highly overall correlated with Infant mortality and 6 other fieldsHigh correlation
ln Minimum Wage is highly overall correlated with Infant mortality and 6 other fieldsHigh correlation
Prevalence of moderate or severe food insecurity in the total population (percent) (2022) is highly overall correlated with Infant mortality and 7 other fieldsHigh correlation
ideal temperature? is highly overall correlated with temperature and 1 other fieldsHigh correlation
Country has unique valuesUnique
GDP has unique valuesUnique
Population has unique valuesUnique
GDP per capita has unique valuesUnique
ln GDP per capita has unique valuesUnique

Reproduction

Analysis started2023-11-22 13:57:12.117103
Analysis finished2023-11-22 13:57:34.466573
Duration22.35 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Country
Text

UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:34.783098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.9076923
Min length4

Characters and Unicode

Total characters1737
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
republic 6
 
2.3%
and 6
 
2.3%
the 5
 
1.9%
of 4
 
1.6%
saint 3
 
1.2%
united 3
 
1.2%
guinea 3
 
1.2%
south 3
 
1.2%
korea 2
 
0.8%
sudan 2
 
0.8%
Other values (216) 221
85.7%
2023-11-22T21:57:35.251811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 262
15.1%
i 153
 
8.8%
n 135
 
7.8%
e 119
 
6.9%
r 94
 
5.4%
o 94
 
5.4%
t 75
 
4.3%
u 68
 
3.9%
63
 
3.6%
l 60
 
3.5%
Other values (42) 614
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1427
82.2%
Uppercase Letter 246
 
14.2%
Space Separator 63
 
3.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 262
18.4%
i 153
10.7%
n 135
9.5%
e 119
 
8.3%
r 94
 
6.6%
o 94
 
6.6%
t 75
 
5.3%
u 68
 
4.8%
l 60
 
4.2%
s 54
 
3.8%
Other values (16) 313
21.9%
Uppercase Letter
ValueCountFrequency (%)
S 30
 
12.2%
M 20
 
8.1%
C 19
 
7.7%
B 19
 
7.7%
A 16
 
6.5%
T 15
 
6.1%
N 14
 
5.7%
G 14
 
5.7%
L 12
 
4.9%
I 11
 
4.5%
Other values (14) 76
30.9%
Space Separator
ValueCountFrequency (%)
63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1673
96.3%
Common 64
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 262
15.7%
i 153
 
9.1%
n 135
 
8.1%
e 119
 
7.1%
r 94
 
5.6%
o 94
 
5.6%
t 75
 
4.5%
u 68
 
4.1%
l 60
 
3.6%
s 54
 
3.2%
Other values (40) 559
33.4%
Common
ValueCountFrequency (%)
63
98.4%
- 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 262
15.1%
i 153
 
8.8%
n 135
 
7.8%
e 119
 
6.9%
r 94
 
5.4%
o 94
 
5.4%
t 75
 
4.3%
u 68
 
3.9%
63
 
3.6%
l 60
 
3.5%
Other values (42) 614
35.3%

Agricultural Land( %)
Real number (ℝ)

Distinct175
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39019735
Minimum0.006
Maximum0.826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:35.351762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.006
5-th percentile0.0523
Q10.22
median0.39638219
Q30.5485
95-th percentile0.745
Maximum0.826
Range0.82
Interquartile range (IQR)0.3285

Descriptive statistics

Standard deviation0.21526123
Coefficient of variation (CV)0.55167272
Kurtosis-0.89418815
Mean0.39019735
Median Absolute Deviation (MAD)0.16538219
Skewness0.085962116
Sum76.088484
Variance0.046337399
MonotonicityNot monotonic
2023-11-22T21:57:35.433185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.174 3
 
1.5%
0.393 2
 
1.0%
0.233 2
 
1.0%
0.734 2
 
1.0%
0.345 2
 
1.0%
0.311 2
 
1.0%
0.715 2
 
1.0%
0.256 2
 
1.0%
0.287 2
 
1.0%
0.027 2
 
1.0%
Other values (165) 174
89.2%
ValueCountFrequency (%)
0.006 1
0.5%
0.009 1
0.5%
0.014 1
0.5%
0.026 1
0.5%
0.027 2
1.0%
0.034 1
0.5%
0.038 1
0.5%
0.039 1
0.5%
0.046 1
0.5%
0.055 1
0.5%
ValueCountFrequency (%)
0.826 1
0.5%
0.808 1
0.5%
0.804 1
0.5%
0.798 1
0.5%
0.792 1
0.5%
0.777 1
0.5%
0.776 1
0.5%
0.764 1
0.5%
0.758 1
0.5%
0.752 1
0.5%

Co2-Emissions per ton
Real number (ℝ)

HIGH CORRELATION 

Distinct191
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171428.02
Minimum-35211.713
Maximum9893038
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)3.1%
Memory size1.6 KiB
2023-11-22T21:57:35.524791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-35211.713
5-th percentile125.9
Q12017
median10902
Q362945.5
95-th percentile550423.3
Maximum9893038
Range9928249.7
Interquartile range (IQR)60928.5

Descriptive statistics

Standard deviation824292.38
Coefficient of variation (CV)4.8083876
Kurtosis106.47943
Mean171428.02
Median Absolute Deviation (MAD)10755
Skewness9.7427953
Sum33428464
Variance6.7945792 × 1011
MonotonicityNot monotonic
2023-11-22T21:57:35.616804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
495 2
 
1.0%
28284 2
 
1.0%
2017 2
 
1.0%
143 2
 
1.0%
7536 1
 
0.5%
120369 1
 
0.5%
-28775.82801 1
 
0.5%
41023 1
 
0.5%
63457 1
 
0.5%
201150 1
 
0.5%
Other values (181) 181
92.8%
ValueCountFrequency (%)
-35211.71307 1
0.5%
-34771.36743 1
0.5%
-33320.76259 1
0.5%
-31998.8753 1
0.5%
-28775.82801 1
0.5%
-5142.320587 1
0.5%
11 1
0.5%
51 1
0.5%
66 1
0.5%
121 1
0.5%
ValueCountFrequency (%)
9893038 1
0.5%
5006302 1
0.5%
2407672 1
0.5%
1732027 1
0.5%
1135886 1
0.5%
727973 1
0.5%
661710 1
0.5%
620302 1
0.5%
563449 1
0.5%
563325 1
0.5%

CPI
Real number (ℝ)

Distinct192
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.1734
Minimum99.03
Maximum4583.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:35.707068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum99.03
5-th percentile105.306
Q1114.47
median129.61
Q3173.3
95-th percentile264.318
Maximum4583.71
Range4484.68
Interquartile range (IQR)58.83

Descriptive statistics

Standard deviation380.16487
Coefficient of variation (CV)1.9990433
Kurtosis103.01611
Mean190.1734
Median Absolute Deviation (MAD)19.11
Skewness9.7580996
Sum37083.813
Variance144525.33
MonotonicityNot monotonic
2023-11-22T21:57:35.993148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.55 2
 
1.0%
110.62 2
 
1.0%
106.58 2
 
1.0%
149.9 1
 
0.5%
118.17 1
 
0.5%
162.74 1
 
0.5%
109.32 1
 
0.5%
267.51 1
 
0.5%
197.5525787 1
 
0.5%
195.1885775 1
 
0.5%
Other values (182) 182
93.3%
ValueCountFrequency (%)
99.03 1
0.5%
99.55 2
1.0%
99.7 1
0.5%
101.87 1
0.5%
102.51 1
0.5%
103.62 1
0.5%
103.87 1
0.5%
104.57 1
0.5%
104.9 1
0.5%
105.48 1
0.5%
ValueCountFrequency (%)
4583.71 1
0.5%
2740.27 1
0.5%
1344.19 1
0.5%
550.93 1
0.5%
418.34 1
0.5%
294.66 1
0.5%
288.57 1
0.5%
281.66 1
0.5%
268.36 1
0.5%
267.51 1
0.5%

GDP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7543339 × 1011
Minimum47271463
Maximum2.14277 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:36.074417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47271463
5-th percentile8.4307407 × 108
Q18.4768007 × 109
median3.8145289 × 1010
Q32.3589006 × 1011
95-th percentile1.7674254 × 1012
Maximum2.14277 × 1013
Range2.1427653 × 1013
Interquartile range (IQR)2.2741326 × 1011

Descriptive statistics

Standard deviation2.1611025 × 1012
Coefficient of variation (CV)4.5455421
Kurtosis78.864936
Mean4.7543339 × 1011
Median Absolute Deviation (MAD)3.6080287 × 1010
Skewness8.623301
Sum9.2709511 × 1013
Variance4.670364 × 1024
MonotonicityNot monotonic
2023-11-22T21:57:36.164260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.910135383 × 10101
 
0.5%
2122450630 1
 
0.5%
1.252091529 × 10101
 
0.5%
1.292814512 × 10101
 
0.5%
4.481204289 × 10111
 
0.5%
3.21 × 10101
 
0.5%
1.022078107 × 10101
 
0.5%
4.033363636 × 10111
 
0.5%
7.698309493 × 10101
 
0.5%
3.044 × 10111
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
47271463 1
0.5%
133000000 1
0.5%
194647202 1
0.5%
221278000 1
0.5%
283994900 1
0.5%
401932279 1
0.5%
429016605 1
0.5%
450353314 1
0.5%
596033333 1
0.5%
825385185 1
0.5%
ValueCountFrequency (%)
2.14277 × 10131
0.5%
1.991 × 10131
0.5%
5.081769542 × 10121
0.5%
3.845630031 × 10121
0.5%
2.827113185 × 10121
0.5%
2.715518274 × 10121
0.5%
2.611 × 10121
0.5%
2.029 × 10121
0.5%
2.001244392 × 10121
0.5%
1.839758041 × 10121
0.5%

Population
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39831238
Minimum836
Maximum1.4257758 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:36.255893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum836
5-th percentile75541.8
Q12004434
median8877067
Q328885316
95-th percentile1.2607779 × 108
Maximum1.4257758 × 109
Range1.425775 × 109
Interquartile range (IQR)26880882

Descriptive statistics

Standard deviation1.48892 × 108
Coefficient of variation (CV)3.7380709
Kurtosis76.911017
Mean39831238
Median Absolute Deviation (MAD)8295695
Skewness8.4809088
Sum7.7670915 × 109
Variance2.2168826 × 1016
MonotonicityNot monotonic
2023-11-22T21:57:36.339356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38041754 1
 
0.5%
182790 1
 
0.5%
6545502 1
 
0.5%
23310715 1
 
0.5%
200963599 1
 
0.5%
25666161 1
 
0.5%
1836713 1
 
0.5%
5347896 1
 
0.5%
5266535 1
 
0.5%
216565318 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
836 1
0.5%
10084 1
0.5%
11646 1
0.5%
18233 1
0.5%
33860 1
0.5%
38019 1
0.5%
38964 1
0.5%
52823 1
0.5%
58791 1
0.5%
71808 1
0.5%
ValueCountFrequency (%)
1425775850 1
0.5%
1425671352 1
0.5%
328239523 1
0.5%
270203917 1
0.5%
216565318 1
0.5%
212559417 1
0.5%
200963599 1
0.5%
167310838 1
0.5%
144373535 1
0.5%
126226568 1
0.5%

Infant mortality
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.347941
Minimum1.4
Maximum84.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:36.433258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.27
Q16.1
median14.349368
Q332.25
95-th percentile62.18
Maximum84.5
Range83.1
Interquartile range (IQR)26.15

Descriptive statistics

Standard deviation19.30359
Coefficient of variation (CV)0.9042366
Kurtosis0.6419415
Mean21.347941
Median Absolute Deviation (MAD)10.649368
Skewness1.1669734
Sum4162.8486
Variance372.62859
MonotonicityNot monotonic
2023-11-22T21:57:36.517033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 4
 
2.1%
3.1 4
 
2.1%
6.1 4
 
2.1%
9.8 3
 
1.5%
2.7 3
 
1.5%
13.6 3
 
1.5%
3.3 3
 
1.5%
6.4 3
 
1.5%
2.6 3
 
1.5%
12.4 3
 
1.5%
Other values (140) 162
83.1%
ValueCountFrequency (%)
1.4 1
 
0.5%
1.5 1
 
0.5%
1.7 2
1.0%
1.8 1
 
0.5%
1.9 2
1.0%
2.1 2
1.0%
2.2 1
 
0.5%
2.3 2
1.0%
2.5 1
 
0.5%
2.6 3
1.5%
ValueCountFrequency (%)
84.5 1
0.5%
78.5 1
0.5%
76.6 1
0.5%
75.7 1
0.5%
71.4 1
0.5%
68.2 1
0.5%
65.7 1
0.5%
64.9 1
0.5%
63.7 1
0.5%
62.6 1
0.5%

Minimum wage
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4509569
Minimum-1.7495836
Maximum19.716998
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.5%
Memory size1.6 KiB
2023-11-22T21:57:36.603226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.7495836
5-th percentile0.167
Q10.505
median1.16
Q32.965
95-th percentile10.032
Maximum19.716998
Range21.466581
Interquartile range (IQR)2.46

Descriptive statistics

Standard deviation3.2032023
Coefficient of variation (CV)1.3069191
Kurtosis5.648526
Mean2.4509569
Median Absolute Deviation (MAD)0.78
Skewness2.2370792
Sum477.93659
Variance10.260505
MonotonicityNot monotonic
2023-11-22T21:57:36.685105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
1.5%
0.41 3
 
1.5%
0.36 2
 
1.0%
1.28 2
 
1.0%
0.29 2
 
1.0%
1.53 2
 
1.0%
0.4 2
 
1.0%
1.57 2
 
1.0%
0.34 2
 
1.0%
0.31 2
 
1.0%
Other values (149) 173
88.7%
ValueCountFrequency (%)
-1.749583628 1
0.5%
0.01 2
1.0%
0.05 2
1.0%
0.09 2
1.0%
0.12 1
0.5%
0.13 1
0.5%
0.16 1
0.5%
0.17 1
0.5%
0.18 1
0.5%
0.21 1
0.5%
ValueCountFrequency (%)
19.71699758 1
0.5%
13.59 1
0.5%
13.05 1
0.5%
11.72 1
0.5%
11.49 1
0.5%
11.16 1
0.5%
10.79 1
0.5%
10.31 1
0.5%
10.29 1
0.5%
10.13 1
0.5%

Unemployment rate
Real number (ℝ)

Distinct183
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.068170401
Minimum-0.051420577
Maximum0.2818
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.5%
Memory size1.6 KiB
2023-11-22T21:57:36.768513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.051420577
5-th percentile0.01323
Q10.03485
median0.0556
Q30.0895
95-th percentile0.17065
Maximum0.2818
Range0.33322058
Interquartile range (IQR)0.05465

Descriptive statistics

Standard deviation0.049256767
Coefficient of variation (CV)0.72255358
Kurtosis2.2338466
Mean0.068170401
Median Absolute Deviation (MAD)0.0244
Skewness1.3080811
Sum13.293228
Variance0.0024262291
MonotonicityNot monotonic
2023-11-22T21:57:36.850326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0459 3
 
1.5%
0.0434 2
 
1.0%
0.042 2
 
1.0%
0.0411 2
 
1.0%
0.0556 2
 
1.0%
0.0536 2
 
1.0%
0.0246 2
 
1.0%
0.0332 2
 
1.0%
0.1185 2
 
1.0%
0.0633 2
 
1.0%
Other values (173) 174
89.2%
ValueCountFrequency (%)
-0.05142057702 1
0.5%
0.0009 1
0.5%
0.0047 1
0.5%
0.0058 1
0.5%
0.0063 1
0.5%
0.0068 1
0.5%
0.0071 1
0.5%
0.0075 1
0.5%
0.0103 1
0.5%
0.0112 1
0.5%
ValueCountFrequency (%)
0.2818 1
0.5%
0.2341 1
0.5%
0.2071 1
0.5%
0.2027 1
0.5%
0.2 1
0.5%
0.1888 1
0.5%
0.1856 1
0.5%
0.1842 1
0.5%
0.1819 1
0.5%
0.1724 1
0.5%
Distinct164
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62883923
Minimum0.38
Maximum0.868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:36.936419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.38
5-th percentile0.4512
Q10.5725
median0.62509616
Q30.688
95-th percentile0.7984
Maximum0.868
Range0.488
Interquartile range (IQR)0.1155

Descriptive statistics

Standard deviation0.10080756
Coefficient of variation (CV)0.16030736
Kurtosis-0.10238085
Mean0.62883923
Median Absolute Deviation (MAD)0.058096164
Skewness-0.033875857
Sum122.62365
Variance0.010162163
MonotonicityNot monotonic
2023-11-22T21:57:37.024252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72 3
 
1.5%
0.688 3
 
1.5%
0.651 3
 
1.5%
0.598 2
 
1.0%
0.536 2
 
1.0%
0.565 2
 
1.0%
0.724 2
 
1.0%
0.621 2
 
1.0%
0.664 2
 
1.0%
0.64 2
 
1.0%
Other values (154) 172
88.2%
ValueCountFrequency (%)
0.38 1
0.5%
0.393 1
0.5%
0.412 1
0.5%
0.42 1
0.5%
0.43 1
0.5%
0.431 1
0.5%
0.433 1
0.5%
0.437 1
0.5%
0.441 1
0.5%
0.447 1
0.5%
ValueCountFrequency (%)
0.868 1
0.5%
0.861 1
0.5%
0.838 2
1.0%
0.837 1
0.5%
0.834 1
0.5%
0.831 1
0.5%
0.823 1
0.5%
0.821 1
0.5%
0.804 1
0.5%
0.796 1
0.5%

temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct192
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.264507
Minimum-13.3619
Maximum44.422159
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)2.1%
Memory size1.6 KiB
2023-11-22T21:57:37.112900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-13.3619
5-th percentile2.938
Q114.305
median22.311695
Q326.55
95-th percentile30.585392
Maximum44.422159
Range57.784058
Interquartile range (IQR)12.245

Descriptive statistics

Standard deviation8.7159887
Coefficient of variation (CV)0.43011107
Kurtosis0.849479
Mean20.264507
Median Absolute Deviation (MAD)5.298305
Skewness-0.89190593
Sum3951.5789
Variance75.968459
MonotonicityNot monotonic
2023-11-22T21:57:37.185721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.66 2
 
1.0%
26.42 2
 
1.0%
11.38 2
 
1.0%
26.24511987 1
 
0.5%
27.53 1
 
0.5%
31.42 1
 
0.5%
28.96 1
 
0.5%
20.54966914 1
 
0.5%
0.15 1
 
0.5%
29.8 1
 
0.5%
Other values (182) 182
93.3%
ValueCountFrequency (%)
-13.36189953 1
0.5%
-4.21 1
0.5%
-3.57 1
0.5%
-0.29 1
0.5%
0.15 1
0.5%
0.1616542121 1
0.5%
0.9276159539 1
0.5%
0.93 1
0.5%
2.11 1
0.5%
2.7 1
0.5%
ValueCountFrequency (%)
44.42215869 1
0.5%
32.50862436 1
0.5%
32.15 1
0.5%
32.05574612 1
0.5%
31.42 1
0.5%
31.36 1
0.5%
31.31 1
0.5%
30.98 1
0.5%
30.92 1
0.5%
30.78 1
0.5%

ideal temperature?
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
105 
0
90 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 105
53.8%
0 90
46.2%

Length

2023-11-22T21:57:37.265688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-22T21:57:37.326778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 105
53.8%
0 90
46.2%

Most occurring characters

ValueCountFrequency (%)
1 105
53.8%
0 90
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 105
53.8%
0 90
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 105
53.8%
0 90
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 105
53.8%
0 90
46.2%
Distinct187
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1169.9656
Minimum18.1
Maximum3240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:37.399071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile123.8
Q1596.5
median1118
Q31634
95-th percentile2676.8
Maximum3240
Range3221.9
Interquartile range (IQR)1037.5

Descriptive statistics

Standard deviation768.99224
Coefficient of variation (CV)0.65727764
Kurtosis-0.19427287
Mean1169.9656
Median Absolute Deviation (MAD)525
Skewness0.67331127
Sum228143.3
Variance591349.07
MonotonicityNot monotonic
2023-11-22T21:57:37.667316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 2
 
1.0%
1500 2
 
1.0%
282 2
 
1.0%
788 2
 
1.0%
250 2
 
1.0%
2200 2
 
1.0%
1274 2
 
1.0%
900 2
 
1.0%
327 1
 
0.5%
494 1
 
0.5%
Other values (177) 177
90.8%
ValueCountFrequency (%)
18.1 1
0.5%
56 1
0.5%
59 1
0.5%
74 1
0.5%
78 1
0.5%
83 1
0.5%
89 1
0.5%
92 1
0.5%
111 1
0.5%
121 1
0.5%
ValueCountFrequency (%)
3240 1
0.5%
3200 1
0.5%
3142 1
0.5%
3028 1
0.5%
2928 1
0.5%
2926 1
0.5%
2880 1
0.5%
2875 1
0.5%
2722 1
0.5%
2702 1
0.5%

Gini's index
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.872466
Minimum19.505496
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:37.749979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.505496
5-th percentile26.07
Q132.35
median36.8
Q340.7
95-th percentile50.62
Maximum63
Range43.494504
Interquartile range (IQR)8.35

Descriptive statistics

Standard deviation7.0517266
Coefficient of variation (CV)0.1912464
Kurtosis1.0485552
Mean36.872466
Median Absolute Deviation (MAD)4.1
Skewness0.62239483
Sum7190.1309
Variance49.726848
MonotonicityNot monotonic
2023-11-22T21:57:37.845248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.7 4
 
2.1%
36.8 3
 
1.5%
38.8 3
 
1.5%
36 3
 
1.5%
26 3
 
1.5%
34.2 3
 
1.5%
31.7 3
 
1.5%
37.1 2
 
1.0%
40.5 2
 
1.0%
38.6 2
 
1.0%
Other values (149) 167
85.6%
ValueCountFrequency (%)
19.50549617 1
 
0.5%
21.3067165 1
 
0.5%
23.2 1
 
0.5%
24 1
 
0.5%
24.4 1
 
0.5%
25.6 1
 
0.5%
25.7 1
 
0.5%
26 3
1.5%
26.1 1
 
0.5%
26.2 1
 
0.5%
ValueCountFrequency (%)
63 1
0.5%
59.1 1
0.5%
55.9 1
0.5%
54.6 1
0.5%
53.3 1
0.5%
52.9 1
0.5%
51.5 1
0.5%
51.3 1
0.5%
51.2 1
0.5%
50.9 1
0.5%

GDP per capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean726987.3
Minimum261.24747
Maximum1.3871893 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:37.929314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum261.24747
5-th percentile516.89263
Q11953.9051
median6001.4008
Q317596.015
95-th percentile64917.198
Maximum1.3871893 × 108
Range1.3871867 × 108
Interquartile range (IQR)15642.11

Descriptive statistics

Standard deviation9932777.6
Coefficient of variation (CV)13.662931
Kurtosis194.99746
Mean726987.3
Median Absolute Deviation (MAD)4937.2696
Skewness13.964105
Sum1.4176252 × 108
Variance9.8660071 × 1013
MonotonicityNot monotonic
2023-11-22T21:57:38.012707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502.1154869 1
 
0.5%
11611.41545 1
 
0.5%
1912.903745 1
 
0.5%
554.6009687 1
 
0.5%
2229.858696 1
 
0.5%
1250.673991 1
 
0.5%
5564.713196 1
 
0.5%
75419.63487 1
 
0.5%
14617.40878 1
 
0.5%
1405.580556 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
261.2474725 1
0.5%
305.6890707 1
0.5%
326.0630992 1
0.5%
411.5523404 1
0.5%
441.5056034 1
0.5%
467.9074407 1
0.5%
478.1543714 1
0.5%
491.8047231 1
0.5%
502.1154869 1
0.5%
504.4625434 1
0.5%
ValueCountFrequency (%)
138718934.9 1
0.5%
184396.9868 1
0.5%
172357.4723 1
0.5%
110172.3731 1
0.5%
81993.72715 1
0.5%
77629.98899 1
0.5%
75419.63487 1
0.5%
66944.82551 1
0.5%
65280.68224 1
0.5%
65233.28244 1
0.5%

Human Development Index (2021)
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71939286
Minimum0.385
Maximum1.1464299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:38.104261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.462
Q10.5995
median0.731
Q30.8315
95-th percentile0.9403
Maximum1.1464299
Range0.76142992
Interquartile range (IQR)0.232

Descriptive statistics

Standard deviation0.1535821
Coefficient of variation (CV)0.2134885
Kurtosis-0.67218823
Mean0.71939286
Median Absolute Deviation (MAD)0.117
Skewness-0.20309705
Sum140.28161
Variance0.023587462
MonotonicityNot monotonic
2023-11-22T21:57:38.187899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.745 4
 
2.1%
0.767 3
 
1.5%
0.802 3
 
1.5%
0.875 3
 
1.5%
0.607 3
 
1.5%
0.731 2
 
1.0%
0.72 2
 
1.0%
0.715 2
 
1.0%
0.809 2
 
1.0%
0.925 2
 
1.0%
Other values (155) 169
86.7%
ValueCountFrequency (%)
0.385 1
0.5%
0.394 1
0.5%
0.4 1
0.5%
0.404 1
0.5%
0.4069379359 1
0.5%
0.426 1
0.5%
0.428 1
0.5%
0.446 1
0.5%
0.449 1
0.5%
0.455 1
0.5%
ValueCountFrequency (%)
1.146429925 1
0.5%
0.962 1
0.5%
0.961 1
0.5%
0.959 1
0.5%
0.951 1
0.5%
0.948 1
0.5%
0.947 1
0.5%
0.945 1
0.5%
0.942 1
0.5%
0.941 1
0.5%

ln GDP per capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7460947
Minimum5.5654681
Maximum18.74796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:38.275967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.5654681
5-th percentile6.2477101
Q17.577365
median8.6997482
Q39.7753668
95-th percentile11.080863
Maximum18.74796
Range13.182492
Interquartile range (IQR)2.1980017

Descriptive statistics

Standard deviation1.6329261
Coefficient of variation (CV)0.18670345
Kurtosis5.943986
Mean8.7460947
Median Absolute Deviation (MAD)1.0933248
Skewness1.1229391
Sum1705.4885
Variance2.6664476
MonotonicityNot monotonic
2023-11-22T21:57:38.367923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.218830147 1
 
0.5%
9.359743984 1
 
0.5%
7.556377652 1
 
0.5%
6.31824888 1
 
0.5%
7.709693498 1
 
0.5%
7.131437877 1
 
0.5%
8.624200725 1
 
0.5%
11.23082293 1
 
0.5%
9.589968479 1
 
0.5%
7.248205703 1
 
0.5%
Other values (185) 185
94.9%
ValueCountFrequency (%)
5.565468129 1
0.5%
5.722568476 1
0.5%
5.787090919 1
0.5%
6.019936206 1
0.5%
6.090190712 1
0.5%
6.1482705 1
0.5%
6.169933633 1
0.5%
6.198081733 1
0.5%
6.218830147 1
0.5%
6.223493592 1
0.5%
ValueCountFrequency (%)
18.74796039 1
0.5%
12.12484625 1
0.5%
12.05732593 1
0.5%
11.60980145 1
0.5%
11.31439803 1
0.5%
11.25970909 1
0.5%
11.23082293 1
0.5%
11.11162406 1
0.5%
11.08645144 1
0.5%
11.08572508 1
0.5%

ln Minimum Wage
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5867271
Minimum0.33647224
Maximum4.4367515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-22T21:57:38.459923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.33647224
5-th percentile0.81957359
Q11.8082888
median2.6637059
Q33.4734207
95-th percentile4.1300237
Maximum4.4367515
Range4.1002793
Interquartile range (IQR)1.6651319

Descriptive statistics

Standard deviation1.058028
Coefficient of variation (CV)0.40902188
Kurtosis-0.97737735
Mean2.5867271
Median Absolute Deviation (MAD)0.85541712
Skewness-0.26156629
Sum504.41179
Variance1.1194232
MonotonicityNot monotonic
2023-11-22T21:57:38.539283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.280933845 4
 
2.1%
1.131402111 4
 
2.1%
1.808288771 4
 
2.1%
2.282382386 3
 
1.5%
0.993251773 3
 
1.5%
2.610069793 3
 
1.5%
1.193922468 3
 
1.5%
1.85629799 3
 
1.5%
0.955511445 3
 
1.5%
2.517696473 3
 
1.5%
Other values (140) 162
83.1%
ValueCountFrequency (%)
0.3364722366 1
 
0.5%
0.4054651081 1
 
0.5%
0.5306282511 2
1.0%
0.5877866649 1
 
0.5%
0.6418538862 2
1.0%
0.7419373447 2
1.0%
0.7884573604 1
 
0.5%
0.8329091229 2
1.0%
0.9162907319 1
 
0.5%
0.955511445 3
1.5%
ValueCountFrequency (%)
4.436751534 1
0.5%
4.363098625 1
0.5%
4.338597077 1
0.5%
4.32677816 1
0.5%
4.268297869 1
0.5%
4.222444565 1
0.5%
4.185098925 1
0.5%
4.172847624 1
0.5%
4.154184563 1
0.5%
4.136765278 1
0.5%
Distinct176
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.71856
Minimum-119.39563
Maximum92.2
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)2.1%
Memory size1.6 KiB
2023-11-22T21:57:38.621759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-119.39563
5-th percentile5.58
Q113
median33.087829
Q353.514636
95-th percentile82.18
Maximum92.2
Range211.59563
Interquartile range (IQR)40.514636

Descriptive statistics

Standard deviation28.886102
Coefficient of variation (CV)0.83200747
Kurtosis4.0504885
Mean34.71856
Median Absolute Deviation (MAD)20.287829
Skewness-0.76026563
Sum6770.1193
Variance834.40686
MonotonicityNot monotonic
2023-11-22T21:57:38.703966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9 3
 
1.5%
52 2
 
1.0%
10 2
 
1.0%
9.6 2
 
1.0%
15.4 2
 
1.0%
76.6 2
 
1.0%
6 2
 
1.0%
37.1 2
 
1.0%
6.5 2
 
1.0%
11.5 2
 
1.0%
Other values (166) 174
89.2%
ValueCountFrequency (%)
-119.3956264 1
0.5%
-77.42863484 1
0.5%
-36.85142353 1
0.5%
-26.52165755 1
0.5%
2.7 1
0.5%
3.2 1
0.5%
3.6 1
0.5%
5 1
0.5%
5.3 2
1.0%
5.7 1
0.5%
ValueCountFrequency (%)
92.2 1
0.5%
90.3 1
0.5%
88.6 1
0.5%
87.4 1
0.5%
86.91041105 1
0.5%
84.7 1
0.5%
84.6 1
0.5%
84.3 1
0.5%
83.8 1
0.5%
82.6 1
0.5%

Interactions

2023-11-22T21:57:32.783299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.418047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.568146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.913435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.000489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.316790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.550139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.883340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.313669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.785867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.202414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.353061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.694058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.797394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.136030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.334420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.667972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.035233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.487750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.634992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.966980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.070719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.400512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.619244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.936916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.401675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.851097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.269231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.423458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.750141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.850673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.201637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.402619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.737129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.100793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.558072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.719710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.039826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.136079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.472060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.683408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.019008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.496233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.916151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.336279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.486311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.819664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.933390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.284553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.467731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.800055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.167588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.621557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.783732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.101389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.200148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.537764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.733918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.109327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.786290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.982859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.384474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.551196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.883509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.000210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.352093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.542540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.865864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.235078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.686311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.833411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.150726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.265832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.601607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.800191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.207916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.853518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.074018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.452487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.618872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.948085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.067109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.421337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.771915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.927829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.306133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.750684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.917919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.233496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.337558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.683324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.873770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.303466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.927523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.155931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.534033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.701396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.016904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.133590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.483535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.833119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.996457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.367748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.816951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.985049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.293790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.398733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.750095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.133378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.411015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.993519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.236163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.596985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.769092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.067524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.202747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.563135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.902721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.051313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.447453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.883451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.050421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.350681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.460418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.817061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.199531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.489674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.061656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.306239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.664626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.834643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.133591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.273999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.616663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.966717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.116523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.519861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:12.951725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.117034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.418837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.518310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.883476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.269065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.598691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.139657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.386066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.733655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.900298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.200164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.333895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.701016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.035627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.183429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.583127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.016866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.183536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.468012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.583643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.965669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.333287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.705439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.199431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.459527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.802969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.971305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.266641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.567454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.767370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.103365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.250043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.653182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.083515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.253026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.533519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.861609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.033353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.400010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.788208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.279665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.532019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.868069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.033588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.333406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.633986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.833501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.166652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.316329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.733382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.150395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.317083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.605398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.929901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.100096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.466869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.873568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.354663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.602968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:24.952653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.117422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.400207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.716798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.917512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.234028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.383030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.802126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.216911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.383645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.668422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:16.985176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.183330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.533371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:20.937253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.418444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.672028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.019369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.184503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.469038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.783671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.983809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.303351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.446905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.870484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.303655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.634329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.746725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.062381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.250209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.602549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.017946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.501462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.745023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.086248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.416977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.533796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.850757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.050346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.383457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.505792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:33.949985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.366875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.706337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.815796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.135564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.333398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.673785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.092770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.580673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.818693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.154367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.484069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.600270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:28.933652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.133017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.450378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.583146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:34.017382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.440200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.771487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.868882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.199931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.416737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.749977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.161503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.653360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.885370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.219146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.562121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.668961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.000073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.202267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.520772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.633106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:34.085958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:13.500187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:14.836934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:15.937052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:17.249409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:18.483448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:19.804148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:21.231504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:22.716116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:23.952646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:25.285584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:26.616787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:27.717417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:29.066799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:30.267020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:31.600127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-22T21:57:32.699843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-11-22T21:57:38.787562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Agricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperaturePrecipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)ln GDP per capitaln Minimum WagePrevalence of moderate or severe food insecurity in the total population (percent) (2022)ideal temperature?
Agricultural Land( %)1.0000.0880.2700.0830.2760.208-0.2820.070-0.173-0.048-0.2540.082-0.290-0.229-0.2900.2080.1290.000
Co2-Emissions per ton0.0881.0000.0010.9340.751-0.4020.238-0.073-0.187-0.190-0.297-0.1510.3330.4340.333-0.402-0.4550.090
CPI0.2700.0011.000-0.0420.2450.412-0.4740.0670.0200.186-0.0030.207-0.439-0.401-0.4390.4120.3250.062
GDP0.0830.934-0.0421.0000.737-0.4530.315-0.121-0.141-0.200-0.225-0.1560.4360.4910.436-0.453-0.4860.101
Population0.2760.7510.2450.7371.0000.104-0.208-0.139-0.0540.123-0.2060.116-0.225-0.114-0.2250.1040.0010.039
Infant mortality0.208-0.4020.412-0.4530.1041.000-0.7380.0140.1520.6240.0920.466-0.849-0.929-0.8491.0000.8470.487
Minimum wage-0.2820.238-0.4740.315-0.208-0.7381.0000.026-0.105-0.481-0.074-0.3540.8260.7860.826-0.738-0.6200.461
Unemployment rate0.070-0.0730.067-0.121-0.1390.0140.0261.000-0.485-0.153-0.2030.1590.025-0.0100.0250.0140.0630.138
Population: Labor force participation (%)-0.173-0.1870.020-0.141-0.0540.152-0.105-0.4851.0000.2100.2440.209-0.101-0.134-0.1010.1520.1590.235
temperature-0.048-0.1900.186-0.2000.1230.624-0.481-0.1530.2101.0000.2640.490-0.520-0.618-0.5200.6240.6450.829
Precipitation Depth (mm/year)-0.254-0.297-0.003-0.225-0.2060.092-0.074-0.2030.2440.2641.0000.241-0.032-0.097-0.0320.0920.1370.425
Gini's index0.082-0.1510.207-0.1560.1160.466-0.3540.1590.2090.4900.2411.000-0.384-0.493-0.3840.4660.6060.422
GDP per capita-0.2900.333-0.4390.436-0.225-0.8490.8260.025-0.101-0.520-0.032-0.3841.0000.9231.000-0.849-0.7570.000
Human Development Index (2021)-0.2290.434-0.4010.491-0.114-0.9290.786-0.010-0.134-0.618-0.097-0.4930.9231.0000.923-0.929-0.8550.502
ln GDP per capita-0.2900.333-0.4390.436-0.225-0.8490.8260.025-0.101-0.520-0.032-0.3841.0000.9231.000-0.849-0.7570.421
ln Minimum Wage0.208-0.4020.412-0.4530.1041.000-0.7380.0140.1520.6240.0920.466-0.849-0.929-0.8491.0000.8470.491
Prevalence of moderate or severe food insecurity in the total population (percent) (2022)0.129-0.4550.325-0.4860.0010.847-0.6200.0630.1590.6450.1370.606-0.757-0.855-0.7570.8471.0000.433
ideal temperature?0.0000.0900.0620.1010.0390.4870.4610.1380.2350.8290.4250.4220.0000.5020.4210.4910.4331.000

Missing values

2023-11-22T21:57:34.207204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-22T21:57:34.382250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryAgricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperatureideal temperature?Precipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)ln GDP per capitaln Minimum WagePrevalence of moderate or severe food insecurity in the total population (percent) (2022)
0Afghanistan0.5818672.0149.9000001.910135e+1038041754.047.90.430000.1112000.48900026.2451201327.00000041.575334502.1154870.4786.2188303.86911682.60000
1Albania0.4314536.0119.0500001.527808e+102854191.07.81.120000.1233000.55700015.97000001485.00000029.4000005352.8574110.7968.5853862.05412433.10000
2Algeria0.174150006.0151.3600001.699882e+1143053054.020.10.950000.1170000.41200026.300000189.00000027.6000003948.3432790.7458.2810513.00072022.60000
3Andorra0.400469.0182.3052833.154058e+0977142.02.76.630000.0588710.63552515.09322701165.97969832.93327040886.3911620.85810.6185530.99325212.59433
4Angola0.47534693.0261.7300009.463542e+1031825295.051.60.710000.0689000.77500026.69000011010.00000051.3000002973.5911600.5867.9975263.94352279.90000
5Antigua and Barbuda0.205557.0113.8100001.727759e+0997118.05.03.040000.0708450.62164618.88452601030.00000037.24382517790.3093040.7889.7864091.60943836.50000
6Argentina0.543201348.0232.7500004.496634e+1144938712.08.83.350000.0979000.61300016.7500000591.00000042.00000010006.1489740.8429.2109552.17475240.50000
7Armenia0.5895156.0129.1800001.367280e+102957731.011.00.660000.1699000.5560007.5600000562.00000027.9000004622.7334930.7598.4387412.3978958.30000
8Australia0.482375908.0119.8000001.392681e+1225766605.03.113.590000.0527000.65500024.3500001534.00000034.30000054049.8288120.95110.8976621.13140213.20000
9Austria0.32461448.0118.0600004.463147e+118877067.02.96.414450.0467000.6070009.04000001110.00000029.80000050277.2750870.91610.8253081.0647115.30000
CountryAgricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperatureideal temperature?Precipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)ln GDP per capitaln Minimum WagePrevalence of moderate or severe food insecurity in the total population (percent) (2022)
185United Kingdom0.717379025.0119.6200002.827113e+1266834405.03.610.1300000.03850.62810.43000001220.032.60000042300.2671260.92910.6525491.2809345.000000
186United States0.4445006302.0117.2400002.142770e+13328239523.05.67.2500000.14700.6208.0400000715.039.80000065280.6822410.92111.0864511.7227677.900000
187Uruguay0.8266766.0202.9200005.604591e+103461734.06.41.6600000.08730.64020.39000011300.040.80000016190.1269570.8099.6921571.85629816.300000
188Uzbekistan0.62991811.0198.0957345.792129e+1033580650.019.10.2400000.05920.65114.3000000206.034.7729091724.8411340.7277.4528902.94968828.700000
189Vanuatu0.153147.0117.1300009.170589e+08299882.022.31.5600000.04390.69921.21671712000.032.3000003058.0656760.6078.0255383.10458725.500000
190Venezuela0.245164175.02740.2700004.823593e+1128515829.021.40.0100000.08800.59732.05574602044.039.22138816915.4934530.6919.7359853.06339141.862815
191Vietnam0.393192668.0163.5200002.619212e+1196462106.016.50.7300000.02010.77424.66000011821.036.8000002715.2760360.7037.9066492.80336010.000000
192Yemen0.44610609.0157.5800002.691440e+1029161922.042.91.0654790.12910.38024.0062861167.036.700000922.9296420.4556.8275533.75887271.200000
193Zambia0.3215141.0212.3100002.306472e+1017861030.040.40.2400000.11430.74626.75000011020.055.9000001291.3433570.5657.1634383.69883076.000000
194Zimbabwe0.41910983.0105.5100002.144076e+1014645468.033.91.0321800.04950.83125.3700001657.050.3000001463.9859100.5937.2889183.52341577.800000